Perakende Market Lokasyonları için CBS Tabanlı Çok Kriterli AHP Yöntemi ile Optimal Yer Seçimi Analizi: İstanbul Örneği

Yıl 2020, , 1032 - 1050, 31.12.2020

Hasan Can Beyhan , Gizem Eren , Bahadır Aktuğ

https://doi.org/10.35414/akufemubid.803391

Cited By: 6

Öz

Potansiyel zincir market yerlerinin optimal olarak seçilmesi hala ilgi gören bir konudur. Yeni açılan marketlerin önemli bir bölümü düşük karlılık ve Pazar yamyamlığına maruz kalmaktadır. Karlılığı yüksek market yerlerinin seçimi konusunda, Coğrafi Bilgi Sistemleri (CBS) ve büyük veri setleri kullanan sayısal yöntemlerin, kişisel deneyim ve içgüdülere dayalı geleneksel yöntemlere göre daha etkin olduğu ortaya konmuştur. Diğer yandan, veri setinin ölçeği büyüdükçe, olası tutarsızlıkların olma ihtimali artmaktadır. Bu çalışmada, İstanbul’daki zincir market yerlerinin seçimi için mevcut en büyük CBS veri seti kullanılmış ve tüm veri seti için kapsamlı bir tutarlılık analizi gerçekleştirilmiştir. 23 adet öznitelikten oluşan geniş bir veri kümesi çalışmada girdi olarak kullanılmış ve optimal market yerlerinin belirlenmesinde Analitik Hiyerarşi Süreci (AHP) kullanılmış ve sonuçlar İdeal Çözüme Benzerlikle Tercih Düzeni Tekniği (TOPSIS) yöntemi ile de doğrulanmıştır. Sonuçlar, mevcut 7196 market yerinden 218’inin analizler sonucunda yüksek potansiyelli olarak tespit edilen lokasyonlarda yer aldığını, bununla birlikte tespit edilen 75 yüksek potansiyelli alanda hala yeni market açılması için potansiyel olduğunu göstermektedir.

Anahtar Kelimeler

Grid Bazlı Yer Seçimi, Coğrafi Bilgi Sistemi (CBS), Önemli Noktalar (POI), Perakende Zincir Marketler, Analitik Hiyerarşi Süreci (AHP), Coğrafi Karar Destek Sistemi (GeoKDS)

Destekleyen Kurum

Ankara Üniversitesi, Mühendislik Fakültesi, Jeofizik Mühendisliği, Ankara

Teşekkür

Çalışmamızda anket verilerinin kullanımı konusunda yardımlarını esirgemeyen Sn. Cem Pektaş’a teşekkür ederiz.

Optimal Location Selection for Retail Market Locations with GIS Based Multi Criteria AHP Method: The Case of Istanbul

Öz

Optimal selection of possible chain market locations is still an ongoing interest. A significant number of newly founded markets are suffering from low profitability and cannibalism. Contrary to the traditional methods which are based on personal experience or instincts, quantitative methods which employ Geographic Information Systems (GIS) and large data sets have proved to be more efficient to determine the most profitable and market locations. On the other hand, the larger the scale of the data set, the more prone it is to possible inconsistencies. In this study, we employ the largest available GIS data set for selection of chain market locations in İstanbul and carry out an extensive consistency test for the whole data set. A wide range of 23 attributes were incorporated into the analysis and Analytical Hierarchy Process (AHP) was used to determine the optimal locations. The results were also confirmed by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The results show that 218 out of 7196 markets are already located high potential areas determined in the analysis and there is still a potential to establish new markets in 75 high potential areas according to our analysis.

Anahtar Kelimeler

Grid Based Location Selection, Geographic Information System (GIS), Points of Interest (POI), Retail Chain Markets, Analytical Hierarchy Process (AHP), Geographical Decision Support System (GeoKDS)

Kaynakça

• Alarcon,S.,2011. The Trade Credit in the Spanish Agro-Food Industry. New Medit, 10(2), 51-57.
• Arquero, A., Álvarez, M. and Martinez, E., 2009. Decision Management Making by AHP (Analytical Hierarchy Process) Trought GIS Data. Institute of Electrical and Electronics Engineers (IEEE) Latin America Transactions, 7, 101–106.
• Atici, K. B., Şimşek A. B., Ulucan A. and Tosun M. U., 2015. A GIS-Based Multiple Criteria Decision Analysis Approach for Wind Power Plant Site Selection. Utilities Policy, 37, 86–96.
• Bayar, R., 2005. CBS Yardımıyla Modern Alışveriş Merkezleri İçin Uygun Yer Seçimi : Ankara Örneği (Location Choice for Shopping Mall Centers Using GIS: Case Study of Ankara). Coğrafi Bilimler Dergisi/ Turkish Journal Geographical Sciences, 3(2), 19-38.
• Bhushan, N., Rai, K., 2004. Strategic Decision Making: Applying the Analytic Hierarchy Process. Springer-Verlag, 172.
• Dožić, S., and Kalić M., 2014. An AHP Approach to Aircraft Selection Process. Transportation Research Procedia, 3, 165-74.
• Gbanie, S. P., Tengbe P. B., Momoh J.S., Medo J., and Kabba V.T.S., 2013. Modelling Landfill Location Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): Case Study Bo, Southern Sierra Leone. Applied Geography, 36, 3–12.
• Hernández T. and Bennison D., 2000. The Art and Science of Retail Location Decisions. International Journal of Retail & Distribution Management, 28 (8), 357–67.
• Karadeniz M., 2009. The Importance Of Retail Site Selection In Marketing Management And Hypothetical Approaches Used In Site Selection. The Importance of Retail Site Selection In Marketing Management And Hypothetical Approaches Used In Site Selection, 5(3), 79–90
• Kayacan T. and Yirmibeşoğlu F., 2017. Site Selection Criteria for Sports Retail Sector: Istanbul Case. Current Urban Studies, 5(3), 290–304
• Kuo, R. J., Chi, S. C. and Kao, S. S., 2002. A Decision Support System for Selecting Convenience Store Location through Integration of Fuzzy AHP and Artificial Neural Network. Computers in Industry, 47(2), 199–214.
• Kuruüzüm, A., and Atsan N., 2001. Analitik Hiyerarşi Yöntemi ve İşletmecilik Alanındaki Uygulamaları (The Analytic Hierarchy Process Approach And It’s Applications in Business). Akdeniz İktisadi ve İdari Bilimler Fakültesi (İ.İ.B.F.) Dergisi, 1, 83-105.
• Ladle, J. K., Stiller D. and Stiller D., 2009. Retail Site Selection: A New, Innovative Model for Retail Retail Site Selection: A New, Innovative Model for Retail Development Development. Institute of Electrical and Electronics Engineers (IEEE) Latin America Transactions, 7, 101–106.
• Mendes, A. B. and Themido, I. H., 2004. Multi-Outlet Retail Site Location Assessment. International Transactions in Operational Research, 11(1), 1–18.
• Merunka, R. D., 2010. Sales Cannibalism. Wiley International Encyclopedia of Marketing.
• Millet, I. and Wedley, W.C., 2002. Modelling Risk and Uncertainty with the Analytic Hierarchy Process. Journal of Multi-Criteria Decision Analysis, 11(2), 97–107. Monjezi, M., Dehghani, H., Singh, T. N., Sayadi, A.R. and Gholinejad, A., 2010. Application of TOPSIS Method for Selecting the Most Appropriate Blast Design. Arabian Journal of Geosciences, 5(1), 95-101.
• Musyoka, S. M., Mutyauvyu, S. M., Kiema, J. B.K., Karanja, F. N., and Siriba, D. N., 2007. Market Segmentation Using Geographic Information Systems (GIS): A Case Study of the Soft Drink Industry in Kenya. Marketing Intelligence and Planning, 25 (6), 632–42.
• Ozimec, A., Natter M. and Reutterer T., 2010. Geographical Information Systems–Based Marketing Decisions: Effects of Alternative Visualizations on Decision Quality. Journal of Marketing, 74 (6), 94–110.
• Rezaei-Moghaddam, K. and Karami E., 2008. A Multiple Criteria Evaluation of Sustainable Agricultural Development Models Using AHP. Environment, Development and Sustainability, 10 (4), 407–26.
• Roig-Tierno, N., Baviera-Puig, A., Buitrago-Vera J. and Mas-Verdu, F., 2013. The Retail Site Location Decision Process Using GIS and the Analytical Hierarchy Process. Applied Geography, 40, 191–98.
• Rybarczyk, G., and Wu C., 2010. Bicycle Facility Planning Using GIS and Multi-Criteria Decision Analysis. Applied Geography, 30 (2), 282–93.
• Saaty, T. L., 1994. How to Make a Decision: The Analytic Hierarchy Process. Interfaces, 24 (6), 19–43.
• Saaty, T. L., 2008. Decision Making with the Analytic Hierarchy Process - International Journal of Services Sciences - Volume 1, Number 1/2008 - Inderscience Publishers. International Journal of Services Sciences, 1 (1), 83–98.
• Saaty, T. L. and Vargas, L. G., 2001. How to Make A Decision. 34, University of Pittsburgh, Springer Science+Business Media New York, 1–25.
• Suárez-Vega, R., Santos-Peñate, D. R. and Dorta-González, P., 2012. Location Models and GIS Tools for Retail Site Location. Applied Geography, 35 (1-2), 12–22. Supçiller, A. A. and Çapraz, O.,2011. AHP-TOPSIS Yöntemine Dayalı Tedarikçi Seçimi Uygulaması. İstanbul Üniversitesi İktisat Fakültesi Ekonometri ve İstatistik Dergisi, 13, 1-22.
• Şahin, E. K., 2010. Perakende Marketlerin Yer Seçimine Yönelik CBS Uygulaması. III. Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Sempozyumu.
• Şener, Ş., Şener, E., Nas, B. and Karagüzel, R. 2010. Combining AHP with GIS for Landfill Site Selection: A Case Study in the Lake Beyşehir Catchment Area (Konya, Turkey). Waste Management, 30 (11), 2037–46.
• Şentürk, E., Livaoğlu, H., and Yavuz, E., 2017. Çok Kriterli Karar Analizi ile CBS Ortaminda En Uygun Spor Bayi Yer Seçimi İzmit Merkez Örneği. Uygulamalı Yerbilimleri Dergisi, 16 (1), 17–26.
• Türkiye Alışveriş Merkezleri ve Perakendeciler Federasyonu (TAMPF), 2016. Dönüşürken Büyüyen Türkiye Perakende Sektörü Raporu. PricewaterhouseCoopers (PWC), 48-64.
• Timor, M., 2011. Analitik Hiyerarşi Prosesi. Türkmen Kitabevi, İstanbul, 1, 1-304.
• Uyan, M., 2013. GIS-Based Solar Farms Site Selection Using Analytic Hierarchy Process (AHP) in Karapinar Region Konya/Turkey. Renewable and Sustainable Energy Reviews, 28, 11–17.
• Uygurtürk, H., 2012. Finansal Performansın TOPSIS Çok Kriterli Karar Verme Yöntemi İle Belirlenmesi: Ana Metal Sanayi İşletmeleri Üzerine Bir Uygulama. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi (İİBF) Dergisi, 7(2), 95-115.
• Wang, L., Fan, H., and Wang, Y., 2018. Site Selection of Retail Shops Based on Spatial Accessibility and Hybrid BP Neural Network. ISPRS International Journal of Geo-Information, 7(6), 202.
• Wei, J. (2010), TOPSIS Method for Multiple Attribute Decision Making with Incomplete Weight Information in Linguistic Setting. Journal of Convergence Information Technology, 5(10), 181-187.
• Wind, Y. and Saaty, T. L.., 1980. Marketing Applications Of The Analytic Hierarchy Process. Management Science, 26(7), 641–58.
• Wood, S. and Reynolds, J., 2012. Leveraging Locational Insights within Retail Store Development? Assessing the Use of Location Planners’ Knowledge in Retail Marketing. Geoforum, 43(6), 1076–87.
• Yang, J. and Lee, H., 2012. Leveraging Locational Insights within Retail Store Development? Assessing the Use of Location Planners’ Knowledge in Retail Marketing. Geoforum, 43(6), 1076–87.
• Yang, J. and Lee, H., 1997. An AHP Decision Model for Facility Location Selection. Facilities, 15(9-10), 241–54.
• Zeydan, M. and Kayserili, S., 2019. A Rule-Based Decision Support Approach for Site Selection of Automated Teller Machines (ATMs). Intelligent Decision Technologies, 13(2), 161–75.
• 1-http://www.ortakalan.org/gonderi/29378, (02.07.2020)
• 2-https://tr.euronews.com/2019/01/17/2019-kuresel-yasam-kalitesi-endeksi-istanbul-trafikte-en-fazla-zaman-kaybedilen-13-sehir, (21.08.2020)
• 3-https://www.basarsoft.com.tr/, (21.08.2020)